Buffer Nodes Cluster
The Buffer Nodes Cluster in Cortex comprises of Stream, Table, Window, Aggregation, and Trigger Nodes. These Nodes are specifically designed to buffer data according to your requirements, which is why they are grouped under the Buffer Nodes category.
Stream Node is a logical sequence of events in time, defined by a stream definition that includes a name and a set of attributes.
Table Node functions as a stored collection of events with a defined schema. Tables store events in-memory and can connect to external databases for more storage options. They support primary keys and indexes for event uniqueness and searchability, useful for storing and retrieving specific events.
Window Node stores and automatically expire events based on their configuration, reducing memory usage and supporting various aggregation types.
Aggregation Node performs incremental event aggregation over set time periods, ideal for real-time reporting and decision-making. Aggregations can store historical data in external databases and automatically purge data to manage growth
Trigger Node generates timestamp events periodically based on a set interval or cron expression. Triggers are versatile, usable in any query, and useful for periodic query execution or initialization operations.
Each of Buffer Nodes, comes with its own set of configurations.
Each Node's settings are accessible through the Settings button on Node Preview.
Buffer Node settings are compertmentalized into multiple Steps for configurations, allowing for a thematic arrangement of similar configurations.
Defining New Attributes
In the Buffer Nodes Cluster ( just like in the Connector Nodes Cluster) you have the capability to declare and add New Attributes. This feature contrasts with the Processing Nodes Cluster, where the declaration of new attributes is not possible.
When constructing Applications using Node Units; Processing Nodes are then effectively integrated between these nodes, creating what are known as Node Units, which play a significant role in streamlining data processing.
Place within Node Units
Give reference to Node Units page
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